
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "66"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 85 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 81 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	44.061396	10.048175	65.130714	80.499470	20.554500	26.818741	35.065922	32.112915	0.0331	0.0364	0.0032	1.192525	1.195766
2459593	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	44.935291	16.063048	79.704892	85.371682	31.440816	38.101188	25.550727	12.152062	0.0333	0.0356	0.0033	1.231424	1.208197
2459592	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	3.057132	1.561341	6.408303	7.124989	2.947006	1.932071	4.038946	0.680532	0.0270	0.0275	0.0004	nan	nan
2459591	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	29.619053	19.696146	94.694074	99.025785	27.229503	32.609412	13.760992	2.447099	0.0347	0.0377	0.0036	1.152524	1.159409
2459590	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	35.984770	22.733248	87.893322	92.345941	29.481935	35.205676	19.809064	4.691211	0.0337	0.0350	0.0027	1.200921	1.204730
2459589	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	40.980286	20.835334	81.517590	85.575537	17.174366	22.298177	24.187665	17.601376	0.0330	0.0356	0.0031	1.184624	1.161548
2459588	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	56.199919	15.053743	94.576800	104.454358	22.098861	25.060161	24.531462	13.398241	0.0314	0.0353	0.0025	1.176665	1.181590
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	39.706311	20.991358	132.286231	139.431459	-1.594851	8.573211	-4.675000	14.056701	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	38.701014	17.856867	31.040361	32.197937	10.136806	11.678690	25.192880	7.160122	0.0333	0.0366	0.0028	1.247370	1.224275
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	29.838140	26.119032	9.941593	10.701298	9.097776	10.139789	9.152171	5.689573	0.0296	0.0289	0.0008	0.000000	0.000000
2459584	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.778061	24.489530	111.399577	114.825626	15.291319	17.589603	6.700114	5.314351	0.0315	0.0338	0.0030	1.161116	1.251032
2459583	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.281116	18.724243	34.359787	35.362985	20.070569	23.589696	0.264376	0.576231	0.0355	0.0376	0.0060	1.177288	1.204109
2459582	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.375403	16.084021	30.919468	32.131701	24.185100	28.627830	0.281078	0.572597	0.0304	0.0337	0.0039	1.167379	1.169665
2459581	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.692320	22.337727	35.844302	36.272367	19.067399	23.103950	0.513795	0.679725	0.0326	0.0350	0.0051	1.175708	1.180031
2459580	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.259035	16.915883	35.986262	37.141765	21.195441	24.349697	0.032729	0.436548	0.0317	0.0346	0.0044	0.000000	0.000000
2459579	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	2.399353	1.736696	2.854553	2.511455	0.608398	0.765142	1.559206	2.769725	0.0290	0.0276	0.0010	nan	nan
2459577	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.457065	21.578574	33.290099	33.923817	21.152744	24.993554	0.360269	0.625105	0.0304	0.0330	0.0020	1.188023	1.187664
2459576	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459575	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459574	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.640143	15.803108	25.158520	25.857435	20.735425	24.350165	1.314897	0.916148	0.0307	0.0340	0.0025	1.196882	1.195050
2459573	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.202288	16.389430	43.292068	44.459403	24.909042	28.409695	1.139579	1.745002	0.0319	0.0344	0.0031	0.000000	0.000000
2459572	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.978104	16.369995	25.284464	26.020670	16.095250	18.084627	2.506400	3.099859	0.0323	0.0343	0.0036	1.215492	1.216277
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.841641	1.410782	2.768438	2.598057	1.303805	0.732060	1.589530	2.658912	0.0285	0.0284	0.0009	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.971100	20.084349	26.973627	27.563791	1.377703	1.832904	4.001819	4.182437	0.0430	0.0453	0.0052	1.136573	1.143869
2459569	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.250002	19.471759	33.234099	33.784386	10.576978	10.759557	8.435348	9.166387	0.0336	0.0365	0.0054	1.173861	1.175059
2459566	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.843701	15.853563	33.007765	33.724033	19.100121	21.792807	-0.194405	0.006020	0.0307	0.0342	0.0042	1.214978	1.217994
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	-0.160317	-0.160317	-0.179841	-0.179841	0.501946	0.501946	0.023484	0.023484	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.007939	14.182359	35.000633	35.880616	2.458686	2.453815	0.618204	0.884974	0.0319	0.0333	0.0028	1.191775	1.191396
2459563	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.088690	16.203511	66.851014	68.429858	19.870073	22.560192	1.697289	1.905975	0.0339	0.0368	0.0032	1.251566	1.250190
2459562	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.715169	10.249959	17.192255	17.635611	2.557413	2.803149	0.712427	0.855883	0.0337	0.0354	0.0051	1.200909	1.202412
2459561	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.459219	11.030099	16.375854	16.789770	2.848616	3.015592	1.139944	1.289204	0.0333	0.0349	0.0047	1.213563	1.214620
2459560	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.620213	9.969681	16.824611	17.037153	2.630523	3.096435	0.711206	0.609239	0.0319	0.0354	0.0035	1.204150	1.206533
2459559	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459558	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.383496	17.007190	34.464990	35.735114	21.776585	23.495423	1.290328	1.663057	0.0348	0.0363	0.0057	1.193435	1.196428
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0293	0.0283	0.0005	nan	nan
2459556	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.753196	17.304706	37.244677	38.711635	19.627984	21.139071	3.030259	4.107715	0.0325	0.0337	0.0036	1.190805	1.186278
2459554	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0318	0.0338	0.0035	nan	nan
2459553	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0302	0.0329	0.0036	nan	nan
2459552	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.191189	20.643204	50.249560	51.069455	29.238468	32.219602	3.207748	3.277070	0.0310	0.0330	0.0026	0.000000	0.000000
2459551	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.681581	10.014804	20.347408	20.734820	3.514058	4.295614	1.133501	1.072235	0.0327	0.0344	0.0037	1.187855	1.188794
2459550	digital_ok	-	98.35%	98.35%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0405	0.0403	0.0007	nan	nan
2459549	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.456286	15.261880	37.783364	38.566652	21.234849	23.093805	1.145619	1.343623	0.0324	0.0346	0.0036	1.197174	1.195339
2459542	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.103956	21.514530	76.702854	78.195737	1.099763	1.886901	1.193328	1.485636	0.0327	0.0342	0.0024	1.183423	1.183712
2459541	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.436714	10.208178	38.080739	36.928957	219.240882	251.197895	6.845281	15.445739	0.0325	0.0365	0.0038	1.144831	1.141740
2459540	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.694796	15.127982	17.097539	17.775928	4.197323	5.258546	0.725836	1.007943	0.0315	0.0341	0.0018	1.194950	1.193451
2459536	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	23.628911	25.149595	65.387602	66.415823	29.612830	31.737860	2.493898	2.589869	0.0296	0.0327	0.0027	0.909551	0.906731
2459535	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.954870	12.234304	15.886634	16.314128	2.177301	5.471357	0.196095	0.546229	0.0341	0.0352	0.0028	1.215520	1.220575
2459534	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.442551	12.451454	14.767374	15.095349	2.300331	3.859732	0.102061	0.120307	0.0315	0.0331	0.0029	1.244811	1.243686
2459533	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.342358	18.725297	35.209971	36.190939	18.153652	20.834973	1.317388	1.921817	0.0301	0.0329	0.0013	1.258297	1.250314
2459532	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.007701	21.703684	47.447017	49.058855	22.451250	25.594038	0.796036	1.179747	0.0310	0.0334	0.0017	0.000000	0.000000
2459530	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.937270	10.689737	15.008248	15.338062	3.537346	4.311609	0.596975	0.601386	0.0327	0.0340	0.0023	1.192824	1.196593
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	23.352020	26.290600	53.331788	54.733230	40.466234	35.030558	0.101453	0.699018	0.0307	0.0345	0.0034	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	21.199582	21.488132	51.984225	52.355979	39.682454	48.616977	14.583254	13.920725	nan	nan	nan	1.693448	1.690982
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.531931	22.534492	51.105217	53.088780	10.704973	23.548838	1.405165	2.093572	0.0326	0.0346	0.0030	0.914088	0.916809
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	2.230011	1.902735	2.840236	2.852059	0.475069	0.566793	1.061509	2.136077	0.0297	0.0323	0.0010	nan	nan
2459508	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	24.744663	29.387295	49.849624	51.597886	18.094900	15.574374	12.877525	14.925882	0.0308	0.0363	0.0042	0.000000	0.000000
2459505	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.644704	28.433863	46.774823	49.909814	7.091540	9.850528	0.722382	1.917415	0.0311	0.0362	0.0044	1.213982	1.220471
2459503	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.194479	28.185178	82.480604	84.400163	33.762922	33.344043	2.336985	3.110838	0.0277	0.0338	0.0061	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 66 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 66, Node 3:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 66: 2459609

Antenna 66: 2459608

Antenna 66: 2459607

Antenna 66: 2459605

Antenna 66: 2459604

Antenna 66: 2459603

Antenna 66: 2459602

Antenna 66: 2459598

Antenna 66: 2459597

Antenna 66: 2459596

Antenna 66: 2459595

Antenna 66: 2459594

Antenna 66: 2459593

Antenna 66: 2459592

Antenna 66: 2459591

Antenna 66: 2459590

Antenna 66: 2459589

Antenna 66: 2459588

Antenna 66: 2459587

Antenna 66: 2459586

Antenna 66: 2459585

Antenna 66: 2459584

Antenna 66: 2459583

Antenna 66: 2459582

Antenna 66: 2459581

Antenna 66: 2459580

Antenna 66: 2459579

Antenna 66: 2459578

Antenna 66: 2459577

Antenna 66: 2459576

Antenna 66: 2459575

Antenna 66: 2459574

Antenna 66: 2459573

Antenna 66: 2459572

Antenna 66: 2459571

Antenna 66: 2459570

Antenna 66: 2459569

Antenna 66: 2459566

Antenna 66: 2459565

Antenna 66: 2459564

Antenna 66: 2459563

Antenna 66: 2459562

Antenna 66: 2459561

Antenna 66: 2459560

Antenna 66: 2459559

Antenna 66: 2459558

Antenna 66: 2459556

Antenna 66: 2459552

Antenna 66: 2459551

Antenna 66: 2459549

Antenna 66: 2459542

Antenna 66: 2459541

Antenna 66: 2459540

Antenna 66: 2459536

Antenna 66: 2459535

Antenna 66: 2459534

Antenna 66: 2459533

Antenna 66: 2459532

Antenna 66: 2459530

Antenna 66: 2459527

Antenna 66: 2459524

Antenna 66: 2459523

Antenna 66: 2459522

Antenna 66: 2459513

Antenna 66: 2459508

Antenna 66: 2459506

Antenna 66: 2459505

Antenna 66: 2459503

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶